When you decide whether to carry an umbrella, you usually check step by step: Is the sky cloudy? What does the forecast say? If rain is expected, you take it. This logical flow is exactly how a decision tree in machine learning works.
What is a Decision Tree?
A decision tree is a supervised learning algorithm used in both classification (Yes/No, Pass/Fail) and regression (predicting numbers).
Root Node → first question
Branches → possible outcomes
Leaf Nodes → final decision
Think of it as breaking down a large problem into smaller, manageable checks.
How the Algorithm Works
Select the best feature using metrics like Gini Impurity or Information Gain.
Split the dataset based on that feature.
Repeat the process on each subset.
Stop when no further split is possible—the leaf gives the result.
Example: In banking, loan approval might follow a flow—Is the applicant employed? Is the salary above ₹40,000? Is the credit score above 700? Depending on answers, the loan is approved or rejected.
Strengths and Weaknesses
- Easy to interpret
- Works with both numerical and categorical data
- Can overfit data
- Sensitive to small data changes
Applications
Decision trees are widely used in healthcare, banking, retail, marketing, and education.
In short, decision trees bring human-like decision-making into machine learning—structured, logical, and easy to follow.
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